xAI/grok-4.5-text-to-text

grok-4.5-text-to-text

Free to try Grok 4.5 API powered by X-AI for reasoning, writing, coding help, analysis, and scalable production LLM workflows through Flaq AI. Build with one reliable API. Lower cost for coding than Claude Opus 4.8.

Text Chat

Related Grok 4.5 Models

grok-4.5-text-to-texttext-to-text

grok-4.5-image-to-textimage-to-text

Documentation

API Examples

JavaScriptPythoncURL

Submit Example

const response = await fetch('https://api.flaq.ai/api/v1/chat/completions', {
  method: 'POST',
  headers: {
    Authorization: 'Bearer YOUR_API_KEY',
    Accept: 'text/event-stream',
    'Content-Type': 'application/json'
  },
  body: JSON.stringify({
    model: 'grok-4.5-text-to-text',
    messages: [
      {
        role: 'user',
        content: 'Explain the key differences between REST and GraphQL APIs.'
      }
    ],
    stream: true,
    max_tokens: 2048
  })
});

const reader = response.body.getReader();
const decoder = new TextDecoder();
let buffer = '';
let assistantText = '';

while (true) {
  const { done, value } = await reader.read();
  if (done) break;

  buffer += decoder.decode(value, { stream: true });
  const frames = buffer.split('\n\n');
  buffer = frames.pop() || '';

  for (const frame of frames) {
    const lines = frame.split('\n').filter(Boolean);
    let eventName = 'message';
    const dataLines = [];

    for (const line of lines) {
      if (line.startsWith('event:')) {
        eventName = line.slice(6).trim();
      } else if (line.startsWith('data:')) {
        dataLines.push(line.replace(/^data:\s*/, ''));
      }
    }

    const raw = dataLines.join('\n').trim();
    if (raw === '[DONE]') {
      console.log('\nFinal text:', assistantText);
      continue;
    }

    let payload;
    try {
      payload = JSON.parse(raw);
    } catch {
      continue;
    }

    if (eventName === 'error' || payload.error) {
      const msg = payload.error?.message ?? payload.message ?? 'Chat request failed';
      throw new Error(msg);
    }

    const delta = payload.choices?.[0]?.delta;
    if (delta?.content) {
      assistantText += delta.content;
      console.log(assistantText);
    }
  }
}

Submit Example

import json
import requests

response = requests.post(
    'https://api.flaq.ai/api/v1/chat/completions',
    headers={
        'Authorization': 'Bearer YOUR_API_KEY',
        'Accept': 'text/event-stream',
        'Content-Type': 'application/json',
    },
    json={
        'model': 'grok-4.5-text-to-text',
        'messages': [
            {
                'role': 'user',
                'content': 'Explain the key differences between REST and GraphQL APIs.',
            }
        ],
        'stream': True,
        'max_tokens': 2048,
    },
    stream=True,
)

response.raise_for_status()

event_name = 'message'
assistant_text = ''

for raw_line in response.iter_lines(decode_unicode=True):
    if not raw_line:
        event_name = 'message'
        continue

    if raw_line.startswith('event:'):
        event_name = raw_line.replace('event:', '', 1).strip()
        continue

    if raw_line.startswith('data:'):
        raw_data = raw_line.replace('data:', '', 1).strip()
        if raw_data == '[DONE]':
            print('\nFinal text:', assistant_text)
            continue

        payload = json.loads(raw_data)

        if event_name == 'error' or payload.get('error'):
            error = payload.get('error') or payload
            raise RuntimeError(error.get('message', 'Chat request failed'))

        choices = payload.get('choices') or []
        if choices:
            delta = choices[0].get('delta') or {}
            content = delta.get('content')
            if content:
                assistant_text += content
                print(content, end='', flush=True)

Submit Example

curl -N -X POST "https://api.flaq.ai/api/v1/chat/completions" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Accept: text/event-stream" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "grok-4.5-text-to-text",
    "messages": [
      {
        "role": "user",
        "content": "Explain the key differences between REST and GraphQL APIs."
      }
    ],
    "stream": true,
    "max_tokens": 2048
  }'

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Send a message to begin.

Grok 4.5 Text to Text Pricing

Parameters	Price	Original Price	Discount

Token: input	$1.8000 per 1M input tokens	$2.0000 per 1M input tokens	90%
Token: output	$5.4000 per 1M output tokens	$6.0000 per 1M output tokens	90%

README

Grok 4.5 Text-to-Text API for Coding and Knowledge Work

Grok 4.5 Text-to-Text API brings xAI's Grok 4.5 language model to text-based applications through Flaq AI. xAI positions Grok 4.5 for coding, agentic tasks, and knowledge work; this Flaq AI model route provides a focused text-in, text-out chat interface for generation and multi-turn conversations. Developers can send structured message history, stream responses, and control the requested output length without enabling unsupported image or file inputs on this variant.

Key Features of Grok 4.5 Text-to-Text API

Text-Based Language Generation: Send text prompts and receive text responses for writing, explanation, analysis, and question-answering workflows.
Coding-Oriented Model Foundation: Use a model that xAI explicitly positions for software engineering and technical knowledge tasks, while validating generated code in your own environment.
Multi-Turn Message Context: Include recent conversation messages so applications can maintain continuity across follow-up questions and iterative tasks.
Streaming Response Support: Consume incremental response events for chat interfaces and other experiences that benefit from progressive output.
Output Length Control: Set a maximum response length to match product, latency, and usage requirements.
Focused Input Contract: Keep this route text-only, with no image or general file attachments exposed by the current Flaq AI configuration.

How to Use Grok 4.5 Text-to-Text API on Flaq AI

Input: Required text content organized as chat messages.
Output: Generated text returned as a complete response or streamed incrementally.
Context: Recent messages can be included for multi-turn conversation continuity.
Controls: An optional maximum output length can be supplied for the response.
Capabilities: Text generation, explanation, coding assistance, structured drafting, and knowledge-oriented responses through the configured chat interface.

Best Use Cases for Grok 4.5 Text-to-Text API Integration

Developer Assistance: Draft, explain, review, or refactor code while keeping human review and automated testing in the workflow.
Technical Question Answering: Build text-based assistants for product, engineering, and internal knowledge scenarios using supplied conversation context.
Writing and Editing: Generate outlines, rewrite drafts, summarize provided text, and adapt content for different audiences.
Structured Analysis: Ask the model to compare options, organize requirements, or turn unstructured text into a clearer response format.
Conversational Applications: Power multi-turn chat experiences that only require text input and text output.

Note Model responses can be incomplete or incorrect, especially for factual, legal, medical, financial, or production-code decisions. Verify important claims and test generated code before relying on the output. This Flaq AI variant does not expose image input, file input, or web search.

Grok 4.5 Text-to-Text vs Alternatives: Comparative Analysis

Grok 4.5 Text-to-Text vs. Grok 4.5 Image-to-Text The Text-to-Text route requires text input and is intended for text-only conversations. Choose the Image-to-Text route when a request needs one supported image as visual context.
Grok 4.5 vs. Earlier Grok Models xAI positions Grok 4.5 as its newer model for coding, agentic tasks, and knowledge work. Actual results still depend on the prompt, evaluation criteria, and the features exposed by the integration.
Grok 4.5 vs. OpenAI Models Both model families can support text generation and coding workflows through APIs. Compare them with representative prompts, output requirements, latency expectations, and current pricing rather than assuming one model is universally stronger.
Grok 4.5 vs. Anthropic Claude Models Claude models and Grok 4.5 offer different model behavior and platform ecosystems. The better fit depends on task-specific evaluation and the input or tool features required by the application.
Grok 4.5 vs. Self-Hosted Language Models Self-hosted models provide infrastructure control but require deployment and maintenance. Flaq AI offers a managed chat API route for teams that prefer hosted access to Grok 4.5.